EEG emotion recognition is one of the interesting and challenging tasks in the research based emotion human–computer interface system. In this paper, a multi-reservoirs feature coding continuous label fusion semi-supervised Generative Adversarial Networks (MCLFS-GAN) is proposed by using permutation phase transfer entropy as the EEG signal feature. Firstly, the obtained features are encapsulated in time series, and then the features are sent into multi-reservoirs according to the division of brain intra, brain interval or frequency band. After convolution optimization, the feature expression with time sequence relationship is obtained. The generic representation between the features and pseudo effective feature expression are iteratively learned in encoder E and generator G in the generative adversarial way. In addition, the continuous fusion for class intra tags can help to form continuous differences between classes. The experimental results show that the accuracy for the four classification is 81.32% and 54.87% respectively by using SAP and LOSO in DEAP database. Compared with other models, this algorithm can effectively improve the recognition performance.

脑电情感识别是基于研究的情感人机界面系统中有趣且具有挑战性的任务之一。本文以置换相转移熵为脑电信号特征，提出了一种多水库特征编码的连续标签融合半监督广义对抗网络。首先将获得的特征按时间序列进行封装，然后根据脑内，脑间隔或频带的划分将特征发送到多个存储库中。经过卷积优化后，得到具有时间序列关系的特征表达式。在编码器E和生成器G中以生成对抗的方式迭代地学习特征和伪有效特征表达之间的通用表示。此外，类内部标签的连续融合可以帮助在类之间形成连续的差异。实验结果表明，在DEAP数据库中使用SAP和LOSO进行四种分类的准确性分别为81.32％和54.87％。与其他模型相比，该算法可以有效提高识别性能。